Use of the Random Forest classifier to Classify Land Use and Cover using Sentinel 1 and 2 Data in a rural region in the Atlantic Forest biome

Authors

DOI:

https://doi.org/10.5902/2236499487967

Keywords:

Random Forest, Land Use and Land Cover, Image Classification, SAR, Optical

Abstract

The use of land use and land cover maps is essential for environmental monitoring, and for this purpose, it is necessary to use remote sensing techniques. With this in mind, this study aimed to use the Backscatter Coefficient, Polarimetric Decomposition and Interferometric Coherence attributes of the Sentinel 1 sensor and the R, G, B, NIR bands and NDVI and SAVI vegetation indices of the Sentinel 2 sensor to identify the best combination of input variables for the Random Forest (RF) classification algorithm using accuracy, in an area in the “Campos de Cima da Serra,” belonging to the Atlantic Forest biome. The study identified that the use of the three Sentinel 1 attributes together with the optical bands of Sentinel 2 had better accuracy (93%), although the use of only the optical bands obtained 89% accuracy. However, when using only SAR attributes, the lowest accuracy was obtained (67%). The development of this methodology will serve as a basis for the continuation of this research, using more robust techniques such as time series analysis via SITS (Satellite Image Time Series Analysis), with the generation of results for monitoring the Atlantic Forest in the southern region of the country and subsidy for monitoring tests of the pampa biome, due to its high capacity for analyzing time series from different platforms in an open-source package.

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Author Biographies

Andressa Kossmann Ferla, Instituto Nacional de Pesquisas Espaciais https://ror.org/04xbn6x09

Sanitary and Environmental Engineer from the Federal University of Santa Maria - UFSM - FW, Master in Environmental Science and Technology from UFSM-FW, member of the Vegetation Remote Sensing Laboratory - SERVEG (COESU/INPE-MCTI), Scholarship Holder of the Institutional Training Program - PCI - INPE. Has experience in Remote Sensing, Random Forest classifiers, SVM, land use and occupation map, fire risk map, environmental monitoring.

Tatiana Mora Kuplich, Instituto Nacional de Pesquisas Espaciais https://ror.org/04xbn6x09

Graduated in Biological Sciences from the Federal University of Rio Grande do Sul, specialization in Space Organization (DESS in Aménagement des Territoires) from Université Toulouse II in France, Master's degree in Remote Sensing from the National Institute for Space Research (INPE, São José dos Campos ) and PhD from the School of Geography at the University of Southampton in England. In 2002, she was approved in a competition for the Remote Sensing Division (DSR) of INPE in São José dos Campos. In 2008, she transferred to the Southern Space Coordination (COESU), an INPE unit in Santa Maria (RS), where she was Coordinator from August 2018 to October 2020. She completed a Post-Doctorate at the VIPER Laboratory (Visualization and Image Processing for Environmental Research) by Dar Roberts at the University of California Santa Barbara, USA. She is a professor and advisor for the Postgraduate Course in Remote Sensing at the Federal University of Rio Grande do Sul (UFRGS). She works on the study of Brazilian biomes through data and remote sensing techniques.

Igor da Silva Narvaes, Instituto Nacional de Pesquisas Espaciais https://ror.org/04xbn6x09

I hold a degree in Forest Engineering from the Federal University of Santa Maria (2000), a Master's degree in Forest Engineering from the Federal University of Santa Maria (2004) and a PhD in Remote Sensing from the National Institute for Space Research (2010). He is currently a Senior Researcher at the Southern Space Coordination (COESU) of the National Institute for Space Research (INPE). I have experience in Geosciences, Remote Sensing and Geoprocessing emphasis, working mainly on the following topics: remote sensing, forest biomass modelling, classification and optical and SAR polarimetry data.

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Published

2025-03-10

How to Cite

Ferla, A. K., Kuplich, T. M., & Narvaes, I. da S. (2025). Use of the Random Forest classifier to Classify Land Use and Cover using Sentinel 1 and 2 Data in a rural region in the Atlantic Forest biome. Geografia Ensino & Pesquisa, 29. https://doi.org/10.5902/2236499487967

Issue

Vol. 29 (2025): Publicação Contínua

Section

Geoinformação e Sensoriamento Remoto em Geografia

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